Abstract
This UCF invention is an artificial intelligence framework that predicts traffic conditions during hurricane evacuations across large transportation networks. Simply put, the system helps transportation and emergency management agencies anticipate where severe congestion will occur before it happens. Unlike conventional traffic prediction tools that struggle during unusual events such as hurricanes, the technology learns from multiple historical evacuations and can generalize to future storms with different paths, intensities, and evacuation patterns. The system dynamically adapts to changes in sensor availability and road network conditions while delivering network-wide traffic forecasts up to six hours ahead, enabling agencies to make proactive decisions that improve evacuation efficiency and public safety.
Technical Details: The invention combines a Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) architecture to model both spatial and temporal traffic behavior. A master graph representing major transportation corridors is constructed from traffic detector locations and roadway connectivity. Unlike traditional approaches that assume a fixed network structure, the invention dynamically updates the graph at every time step based on active traffic detectors and current network conditions.
The model processes real-time traffic variables, historical traffic trends, time-based indicators, hurricane characteristics, evacuation orders, and population evacuation estimates. Spatial relationships between detectors are extracted through graph learning, while temporal patterns are learned through the LSTM component. Trained on evacuation and traffic data from eleven major Florida hurricanes, the framework can forecast traffic volumes for the next one to six hours while maintaining accuracy even when portions of the sensor network become unavailable. The dynamic graph construction methodology is a key innovation that enables robust performance during highly disruptive emergency situations.
Benefit
Improved evacuation planning: predicts congestion before bottlenecks develop, enabling proactive traffic management.Robust under disrupted conditions: continues operating despite sensor outages, infrastructure disruptions, or incomplete data.Generalizable solution: trained on multiple hurricanes and capable of forecasting traffic for previously unseen storm events.Enhanced public safety: supports faster evacuations, better resource deployment, and reduced evacuation delays.Market Application
Departments of Transportation (DOTs): supports traffic operations centers with advance warning of evacuation congestion and route performanceEmergency Management Agencies: improves evacuation planning, resource allocation, and decision support during hurricanes and other disasters.Transportation Analytics Providers: enables integration into commercial traffic forecasting, intelligent transportation systems, and mobility platforms. Public Safety & Smart Infrastructure: applicable to hurricanes, wildfires, floods, chemical spills, mass evacuations, and future smart-city transportation management system
Brochure
